Backtest 1000s of minute-by-minute trading algorithms for training AI with automated pricing data from: IEX, Tradier and FinViz. Datasets and trading performance automatically published to S3 for building AI training datasets for teaching DNNs how to trade. Runs on Kubernetes and docker-compose. >150 million trading history rows generated from +5000 algorithms. Heads up: Yahoo's Finance API was disabled on 2019-01-03 https://developer.yahoo.com/yql/

Applying Machine Learning and AI Algorithms applied to Trading for better performance and low Std.

Harvard Algorithmic Trading

A computer implementation of a trading card game: Magic: The Gathering. The goal of the project is to implement ai algorithms which can play the game at the same level as a casual human player.

AI-CryptoTrader is a state-of-the-art cryptocurrency trading bot that uses ensemble methods to make trading decisions based on multiple sophisticated algorithms. Built with the latest machine learning and data science techniques, AI-CryptoTrader provides a powerful toolset and advanced trading stratgies for maximizing your cryptocurrency profits.

AI-powered CLI tool: Transform trading research papers into QuantConnect algorithms

Binary Trading AI Bot is a project idea aimed at developing an AI-powered bot for binary trading. The bot utilizes machine learning algorithms to predict the direction of the next candle (whether it will move up or down) with high accuracy.

High frequency AI based algorithmic trading module.

DRL Trading - AI Gold Trading Bot Deep reinforcement learning system for autonomous XAUUSD trading using: - PPO & Dreamer algorithms (PyTorch) - 140+ features: multi-timeframe, macro data, economic events - MetaTrader 5 live trading - 2M steps trained, targeting 80-120% annual returns - Multiple strategies (aggressive/swing/standard)

AI-powered SDK featuring algorithmic trading, backtesting, deployment on 100+ exchanges, and multiple optimization engines.

TradeAI: Empowering Algorithmic Trading with Deep Learning for Cryptocurrency Data. Explore the potential of deep learning in cryptocurrency trading through our full-stack algorithmic trading system, fueled by state-of-the-art transformer models

Form Trading Bot is an advanced algorithmic trading system designed to automate and optimize your trading strategies across multiple financial markets. Leveraging cutting-edge AI and quantitative analysis, it executes trades with precision and speed unmatched by manual trading.

A next-generation AI-powered algorithmic trading platform supporting strategy backtesting, live/paper trading, parameter optimization, and intelligent analysis.

WARNING: using this software will probably lose you money. The AIStockTradingBot will someday handle my Stock Trading Strategy while I am away. The intention is to handling trades and save/analyzing price data based on algorithms. Also providing actionable notifications. Currently this is all based on the TD Ameritrade API but the long term intention is to provide this service to multiple account providers. Also news and social media AI updates are very desired in time.

AI-powered, real-time NSE algorithmic trading engine with Zerodha integration, Redis caching, and live indicator/signal computation.

Archimedes 1 is a bot based sentient based trader, heavily influenced on forked existing bots, with a few enhancements here or there, this was completed to understand how the bots worked to roll the forward in our own manner to our own complete ai based trading system (Archimedes 2:0) This bot watches [followed accounts] tweets and waits for them to mention any publicly traded companies. When they do, sentiment analysis is used determine whether the opinions are positive or negative toward those companies. The bot then automatically executes trades on the relevant stocks according to the expected market reaction. The code is written in Python and is meant to run on a Google Compute Engine instance. It uses the Twitter Streaming APIs (however new version) to get notified whenever tweets within remit are of interest. The entity detection and sentiment analysis is done using Google's Cloud Natural Language API and the Wikidata Query Service provides the company data. The TradeKing (ALLY) API does the stock trading (changed to ALLY). The main module defines a callback where incoming tweets are handled and starts streaming user's feed: def twitter_callback(tweet): companies = analysis.find_companies(tweet) if companies: trading.make_trades(companies) twitter.tweet(companies, tweet) if __name__ == "__main__": twitter.start_streaming(twitter_callback) The core algorithms are implemented in the analysis and trading modules. The former finds mentions of companies in the text of the tweet, figures out what their ticker symbol is, and assigns a sentiment score to them. The latter chooses a trading strategy, which is either buy now and sell at close or sell short now and buy to cover at close. The twitter module deals with streaming and tweeting out the summary. Follow these steps to run the code yourself: 1. Create VM instance Check out the quickstart to create a Cloud Platform project and a Linux VM instance with Compute Engine, then SSH into it for the steps below. The predefined machine type g1-small (1 vCPU, 1.7 GB memory) seems to work well. 2. Set up auth The authentication keys for the different APIs are read from shell environment variables. Each service has different steps to obtain them. Twitter Log in to your Twitter account and create a new application. Under the Keys and Access Tokens tab for your app you'll find the Consumer Key and Consumer Secret. Export both to environment variables: export TWITTER_CONSUMER_KEY="<YOUR_CONSUMER_KEY>" export TWITTER_CONSUMER_SECRET="<YOUR_CONSUMER_SECRET>" If you want the tweets to come from the same account that owns the application, simply use the Access Token and Access Token Secret on the same page. If you want to tweet from a different account, follow the steps to obtain an access token. Then export both to environment variables: export TWITTER_ACCESS_TOKEN="<YOUR_ACCESS_TOKEN>" export TWITTER_ACCESS_TOKEN_SECRET="<YOUR_ACCESS_TOKEN_SECRET>" Google Follow the Google Application Default Credentials instructions to create, download, and export a service account key. export GOOGLE_APPLICATION_CREDENTIALS="/path/to/credentials-file.json" You also need to enable the Cloud Natural Language API for your Google Cloud Platform project. TradeKing (ALLY) Log in to your TradeKing (ALLY account and create a new application. Behind the Details button for your application you'll find the Consumer Key, Consumer Secret, OAuth (Access) Token, and Oauth (Access) Token Secret. Export them all to environment variables: export TRADEKING_CONSUMER_KEY="<YOUR_CONSUMER_KEY>" export TRADEKING_CONSUMER_SECRET="<YOUR_CONSUMER_SECRET>" export TRADEKING_ACCESS_TOKEN="<YOUR_ACCESS_TOKEN>" export TRADEKING_ACCESS_TOKEN_SECRET="<YOUR_ACCESS_TOKEN_SECRET>" Also export your TradeKing (ALLY) account number, which you'll find under My Accounts: export TRADEKING_ACCOUNT_NUMBER="<YOUR_ACCOUNT_NUMBER>" 3. Install dependencies There are a few library dependencies, which you can install using pip: $ pip install -r requirements.txt 4. Run the tests Verify that everything is working as intended by running the tests with pytest using this command: $ export USE_REAL_MONEY=NO && pytest *.py --verbose 5. Run the benchmark The benchmark report shows how the current implementation of the analysis and trading algorithms would have performed against historical data. You can run it again to benchmark any changes you may have made: $ ./benchmark.py > benchmark.md 6. Start the bot Enable real orders that use your money: $ export USE_REAL_MONEY=YES Have the code start running in the background with this command: $ nohup ./main.py & License Archimedes (edits under Invacio) Max Braun Frame under Max Braun, licence under Apache V2 License. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

Identify the software project, create business case, arrive at a problem statement. REQUIREMENT: Window XP, Internet, MS Office, etc. Problem Description: - 1. Introduction of AI and Machine Learning: - Artificial Intelligence applies machine learning, deep learning and other techniques to solve actual problems. Artificial intelligence (AI) brings the genuine human-to-machine interaction. Simply, Machine Learning is the algorithm that give computers the ability to learn from data and then make decisions and predictions, AI refers to idea where machines can execute tasks smartly. It is a faster process in learning the risk factors, and profitable opportunities. They have a feature of learning from their mistakes and experiences. When Machine learning is combined with Artificial Intelligence, it can be a large field to gather an immense amount of information and then rectify the errors and learn from further experiences, developing in a smarter, faster and accuracy handling technique. The main difference between Machine Learning and Artificial Intelligence is , If it is written in python then it is probably machine learning, If it is written in power point then it is artificial intelligence. As there are many existing projects that are implemented using AI and Machine Learning , And one of the project i.e., Bitcoin Price Prediction :- Bitcoin (₿ ) (founder - Satoshi Nakamoto , Ledger start: 3 January 2009 ) is a digital currency, a type of electronic money. It is decentralized advanced cash without a national bank or single chairman that can be sent from client to client on the shared Bitcoin arrange without middle people's requirement. Machine learning models can likely give us the insight we need to learn about the future of Cryptocurrency. It will not tell us the future but it might tell us the general trend and direction to expect the prices to move. These machine learning models predict the future of Bitcoin by coding them out in Python. Machine learning and AI-assisted trading have attracted growing interest for the past few years. this approach is to test the hypothesis that the inefficiency of the cryptocurrency market can be exploited to generate abnormal profits. the application of machine learning algorithms to the cryptocurrency market has been limited so far to the analysis of Bitcoin prices, using random forests , Bayesian neural network , long short-term memory neural network , and other algorithms. 2. Applications/Scope of AI and Machine Learning :- a) Sentiment Analysis :- It is the classification of subjective opinions or emotions (positive, negative, and neutral) within text data using natural language processing. b) It is Characterized as a use of computerized reasoning where accessible data is utilized through calculations to process or help the handling of factual information. BITCOIN PRICE PREDICTION USING AI AND MACHINE LEARNING: - The main aim of this is to find the actual Bitcoin price in US dollars can be predicted. The chance to make a model equipped for anticipating digital currencies fundamentally Bitcoin. # It works the prediction by taking the coinMarkup cap. # CoinMarketCap provides with historical data for Bitcoin price changes, keep a record of all the transactions by recording the amount of coins in circulation and the volume of coins traded in the last 24-hours. # Quandl is used to filter the dataset by using the MAT Lab properties. 3. Problem statement: - Some AI and Machine Learning problem statements are: - a) Data Privacy and Security: Once a company has dug up the data, privacy and security is eye-catching aspect that needs to be taken care of. b) Data Scarcity: The data is a very important aspect of AI, and labeled data is used to train machines to learn and make predictions. c) Data acquisition: In the process of machine learning, a large amount of data is used in the process of training and learning. d) High error susceptibility: In the process of artificial intelligence and machine learning, the high amount of data is used. Some problem statements of Bitcoin Price Prediction using AI and Machine Learning: - a) Experimental Phase Risk: It is less experimental than other counterparts. In addition, relative to traditional assets, its level can be assessed as high because this asset is not intended for conservative investors. b) Technology Risks: There is a technological risk to other cryptocurrencies in the form of the potential appearance of a more advanced cryptocurrency. Investors may simply not notice the moment when their virtual assets lose their real value. c) Price Variability: The variability of the value of cryptocurrency are the large volumes of exchange trading, the integration of Bitcoin with various companies, legislative initiatives of regulatory bodies and many other, sometimes disregarded phenomena. d) Consumer Protection: The property of the irreversibility of transactions in itself has little effect on the risks of investing in Bitcoin as an asset. e) Price Fluctuation Prediction: Since many investors care more about whether the sudden rise or fall is worth following. Bitcoin price often fluctuates by more than 10% (or even more than 30%) at some times. f) Lacks Government Regulation: Regulators in traditional financial markets are basically missing in the field of cryptocurrencies. For instance, fake news frequently affects the decisions of individual investors. g) It is difficult to use large interval data (e.g., day-level, and month-level data) . h) The change time of mining difficulties is much longer. Moreover, do not consider the news information since it is hard to determine the authenticity of a news or predict the occurrence of emergencies.

📈 Base on AI(RL/LSTM) algorithm to trade stock or features

AI-powered algorithmic trading system that combines FinBERT sentiment analysis, spaCy NER, and TimeGPT forecasting to generate BUY/SELL/HOLD signals from real-time financial news.

AI-based Trading assistant based on Binance data and machine learning algorithms.

A proof-of-concept AI-powered hedge fund that simulates trading decisions using multiple AI agents. Designed to explore algorithmic investing through fundamental analysis, technical indicators, sentiment analysis, and risk management.

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Full code of the article: AI-Powered Energy Algorithmic Trading: Integrating Hidden Markov Models with Neural Networks

With WWW being the global platform, various fields inclusive to the same have emerged until hitherto. Due to ever-changing forms of cyber Security, it has become a necessity to classify Malicious websites so as to secure personal content. In this project we have implemented The State-Of-the-Art Decision Tree Machine Learning Models such as Random Forest and Decision Tree to classify URLs as malicious or amiable. Implementation of Classification algorithms for discrete data as well as normal regression model is used in the project. Malevolent URLs have been broadly used to mount different digital assaults including spamming, phishing and malware. Recognition of malignant URLs and distinguishing proof of danger types are basic to upset these assaults. Knowing the sort of a danger empowers assessment of seriousness of the assault and embraces a viable countermeasure. Existing strategies commonly distinguish vindictive URLs of a solitary assault type. In this paper, we propose technique utilizing AI to identify malevolent URLs of all the mainstream assault types. While the World Wide Web has become a stellar application on the Internet, it has likewise gotten a massive danger of digital assaults. Enemies have utilized the Web as a vehicle to convey malignant assaults, for example, phishing, spamming, and malware contamination. For instance, phishing ordinarily includes sending an email apparently from a dependable source to deceive individuals to click a URL (Uniform Resource Locator) contained in the email that joins to a fake page. To address Web-based assaults, an incredible exertion has been coordinated towards identification of noxious URLs. A typical countermeasure is to utilize a boycott of vindictive URLs, which can be built from different sources, particularly human criticisms that are exceptionally precise yet tedious. Boycotting acquires no bogus positives, however is successful just for known noxious URLs. It can't identify obscure malevolent URLs. The very idea of careful match in boycotting these renders it simple to be sidestepped. This shortcoming of blacklisting has been tended to by oddity-based location techniques intended to identify obscure vindictive URLs. In these strategies, a characterization model dependent on discriminative principles or highlights is worked with either information from the earlier or through machine learning. Choice of discriminative standards or highlights assumes a basic function for the presentation of a locator. Online malware assaults become one in everything about chief genuine dangers that need to be tended too frantically. Numerous methodologies that have stood out as promising manners by which of safeguard work, for example, malware grasp utilizing various boycotts. Nonetheless, these standard methodologies ordinarily neglect to watch new assaults due to the adaptability of malignant sites. Consequently, it's hard to deal with state-of-the-art boycotts with data concerning new vindictive sites. Malignant location identification assumes a significant part for a few network protection applications, and unmistakably AI moves toward square measure a promising course. In mix with protection imperatives on information sets of real client traffic, its irksome for scientists and product engineers to measure hostile to malware arrangements against huge scope information sets of practical net traffic. AI strategy [1] region unit utilized so as to characterize the online deals into malignant and benevolent URLs. The appearance of ongoing correspondence innovations has had enormous contact with in the development and advancement of organizations spamming over a few applications just as web based banking, online business, and long range informal communication. In actuality, in the present age it's almost required to have a web presence to run a famous endeavour. Accordingly, the significance of the overall net has ceaselessly been expanding. Unfortunately the mechanical promotions return in expansion to new unobtrusive strategies to assault and trick client. Such assaults grasp noxious sites that sell fake stock, financial extortion by fooling clients into uncovering delicate data that in the long run cause stealing of money or character, or maybe placing in malware inside the clients framework. There square measure a huge kind of procedures to actualize such assaults, similar to explicit hacking attempts, Derive-by abuses, Denial of administration [2], Distributed refusal of administration [1] and bunches of others. Concentrating the changeability of assaults, without a doubt new assault assortments, and furthermore the unnumbered settings inside which such assaults will appears, it's exhausting to style-solid frameworks to find digital security penetrates. The restrictions of customary security the board advancements are getting to an ever increasing extent genuine given this remarkable development of new security dangers, fast changes of new IT advancements, and critical deficiency of security experts. The vast majority of these assaulting strategies are acknowledged through spreading traded off URLs. A primary exploration exertion in pernicious URL recognition has zeroed in on choosing profoundly successful discriminative highlights. Existing techniques were intended to distinguish pernicious URLs of a solitary assault type, for example, spamming, phishing, or malware. In this paper, we propose a strategy utilizing Machine Learning Algorithms on how to distinguish malevolent URLs of all the well known assault types including phishing, spamming and malware contamination, and distinguish the assault types noxious URLs endeavour to dispatch.

AI Algorithmic Trading for the Blockchain

A system which uses AI / Machine Learning algorithms and recommends the appropriate options to buy/sell, so that trader can maximise his profits. It provides advanced technical analysis for profitable stock market trading and accurate buy & sells alerts along with entry and exit levels

Algorithmic Trading with Python

This repository offers an AI-powered crypto trading bot designed for automated trading of Bitcoin and other cryptocurrencies. It provides tools and algorithms for efficient and intelligent trading, maximizing profit and minimizing risk.

.Net algorithmic program that provides trading signals by analysing stocks using multiple AI agents